Turning ideas and prototypes into reliable, production-ready Machine Learning systems that deliver stable, measurable value in real-world conditions.
Requirements
- Proficiency in Python and its frameworks for streaming, batch, and asynchronous data processing
- Solid experience with classic machine learning techniques and algorithms
- Experience using MLOps tools and practices to manage the ML model lifecycle
- Familiarity with technologies like Golang for backend system and infrastructure integration
- Ability to conduct thorough data analysis and preparation
- Strong problem-solving skills with a focus on engineering principles and data-driven reasoning
Responsibilities
- Build and deploy machine learning solutions from end to end, contributing to the full ML delivery cycle
- Conduct data analysis, annotation, and processing as a key part of the ML system design
- Design solutions using common patterns and tools, and propose alternative approaches when needed
- Ensure solutions meet design standards and are reusable, flexible, and extensible through peer reviews
- Deploy features and ensure they work as intended while preventing unintended side effects
- Document technical solutions and ensure all necessary monitoring and support tools are in place
- Solve issues in the engineering design and delivery process and ensure the solution meets key performance indicators and non-functional requirements
Other
- Collaborate with team members, contribute to a community of practice, and promote the reuse of common approaches and technologies
- Excellent communication and collaboration skills, with an ability to seek and validate information from various sources
- A solid understanding of business value and how it connects to delivering features
- A proactive approach to self-development and learning new skills and best practices
- Relocation package offered for candidates from other regions
